Modeling and Predicting Popularity Dynamics via an Influence-based Self-Excited Hawkes Process

Modeling and predicting the popularity dynamics of individual user generated items on online social networks has important implications in a wide range of areas. The challenge of this problem comes from the inequality of the popularity of content and the numerous complex factors. Existing works mainly focus on exploring relevant factors for prediction and fitting the time series of popularity dynamics into certain class of functions, while ignoring the underlying arrival process of attentions. Also, the exogenous effect of user activity variation on the platform has been neglected. In this paper, we propose a probabilistic model using an influence-based self-excited Hawkes process (ISEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures three ingredients: the intrinsic attractiveness of a microblog with exponential time decay, the user-specific triggering effect of each forwardings based on the endogenous influence among users, and the exogenous effect from the platform. We validate the ISEHP model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that our proposed model consistently outperforms existing prediction models.